Advertisement

Towards Personalized Learning Objectives in MOOCs

  • Tobias RohloffEmail author
  • Christoph Meinel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11082)

Abstract

Instead of measuring success in Massive Open Online Courses (MOOCs) based on certification and completion-rates, researchers started to define success with alternative metrics recently, for example by evaluating the intention-behavior gap and goal achievement. Especially self-regulated and goal-oriented learning have been identified as critical skills to be successful in online learning environments with low guidance like MOOCs, but technical support is rare. Therefore, this paper examines the current technical capabilities and limitations of goal-oriented learning in MOOCs. An observational study to explore how well learners in five MOOCs achieved their initial learning objectives was conducted, and the results are compared with similar studies. Afterwards, a concept with a focus on technical feasibility and automation outlines how personalized learning objectives can be supported and implemented on a MOOC platform.

Keywords

Learning objectives MOOCs Goal-oriented learning Self-regulated learning Learning analytics E-learning 

References

  1. 1.
    Che, X., Yang, H., Meinel, C.: Adaptive E-Lecture video outline extraction based on slides analysis. In: Li, F.W.B., Klamma, R., Laanpere, M., Zhang, J., Manjón, B.F., Lau, R.W.H. (eds.) ICWL 2015. LNCS, vol. 9412, pp. 59–68. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25515-6_6CrossRefGoogle Scholar
  2. 2.
    Davis, D., Chen, G., Jivet, I., Hauff, C., Houben, G.: Encouraging metacognition & self-regulation in MOOCs through increased learner feedback. In: Proceedings of the LAK 2016 Workshop on Learning Analytics for Learners, pp. 17–22 (2016). http://ceur-ws.org/Vol-1596/paper3.pdf
  3. 3.
    Davis, D., Jivet, I., Kizilcec, R.F., Chen, G., Hauff, C., Houben, G.-J.: Follow the successful crowd: raising MOOC completion rates through social comparison at scale. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, LAK 2017, pp. 454–463. ACM (2017).  https://doi.org/10.1145/3027385.3027411
  4. 4.
    Emanuel, E.J.: Online education: MOOCs taken by educated few. Nature 503(7476), 342 (2013). https://www.nature.com/articles/503342aCrossRefGoogle Scholar
  5. 5.
    Farsani, M.A., Beikmohammadi, M., Mohebbi, A.: Self-regulated learning, goal-oriented learning, and academic writing performance of undergraduate Iranian EFL learners. In: TESL-EJ, vol. 18(2) (2014). https://eric.ed.gov/?id=EJ1045129
  6. 6.
    Goodyear, P.: Psychological foundations for networked learning. In: Steeples, C., Jones, C. (eds.) Networked Learning: Perspectives and Issues. Computer Supported Cooperative Work, pp. 49–75. Springer, London (2002).  https://doi.org/10.1007/978-1-4471-0181-9_4CrossRefGoogle Scholar
  7. 7.
    Henderikx, M.A., Kreijns, K., Kalz, M.: Refining success and dropout in massive open online courses based on the intention-behavior gap. Distance Educ. 38(3), 353–368 (2017).  https://doi.org/10.1080/01587919.2017.1369006CrossRefGoogle Scholar
  8. 8.
    Hill, P.: Emerging student patterns in MOOCs: a (revised) graphical view (2013). https://mfeldstein.com/emerging-student-patterns-in-moocs-a-revised-graphical-view/
  9. 9.
    Huang, H.-M.: Toward constructivism for adult learners in online learning environments. Br. J. Educ. Technol. 33(1), 27–37 (2002).  https://doi.org/10.1111/1467-8535.00236CrossRefGoogle Scholar
  10. 10.
    Jivet, I., Scheffel, M., Specht, M., Drachsler, H.: License to evaluate: preparing learning analytics dashboards for educational practice. In: Proceedings of the 8th International Conference on Learning Analytics and Knowledge, LAK 2018, pp. 31–40. ACM (2018).  https://doi.org/10.1145/3170358.3170421
  11. 11.
    Jordan, K.: Initial trends in enrolment and completion of massive open online courses. Int. Rev. Res. Open Distrib. Learn. 15(1) (2014).  https://doi.org/10.19173/irrodl.v15i1.1651
  12. 12.
    Joyner, D.A.: Congruency, adaptivity, modularity, and personalization: four experiments in teaching introduction to computing. In: Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, L@S 2017, pp. 307–310. ACM (2017).  https://doi.org/10.1145/3051457.3054011
  13. 13.
    Kizilcec, R.F., Halawa, S.: Attrition and achievement gaps in online learning. In: Proceedings of the Second (2015) ACM Conference on Learning @ Scale, L@S 2015, pp. 57–66. ACM (2015).  https://doi.org/10.1145/2724660.2724680
  14. 14.
    Kizilcec, R.F., Pérez-Sanagustín, M., Maldonado, J.J.: Recommending self- regulated learning strategies does not improve performance in a MOOC. In: Proceedings of the Third (2016) ACM Conference on Learning @ Scale, L@S 2016, pp. 101–104. ACM (2016).  https://doi.org/10.1145/2876034.2893378
  15. 15.
    Kizilcec, R.F., Pérez-Sanagustín, M., Maldonado, J.J.: Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Comput. Educ. 104, 18–33 (2017).  https://doi.org/10.1016/j.compedu.2016.10.001CrossRefGoogle Scholar
  16. 16.
    Lajoie, S.P., Azevedo, R.: Teaching and learning in technology-rich environments. In: Handbook of Educational Psychology. Routledge (2006).  https://doi.org/10.4324/9780203874790.ch35
  17. 17.
    Liyanagunawardena, T.R., Parslow, P., Williams, S.A.: Dropout: MOOC participants’ perspective. In: The Second MOOC European Stakeholders Summit, EMOOCs 2014, pp. 95–100 (2014). http://centaur.reading.ac.uk/36002/
  18. 18.
    Mayes, T., De Freitas, S.: Review of e-learning theories, frameworks and models. In: JISC E-Learning Models Desk Study (2004). http://www.jisc.ac.uk/whatwedo/programmes/elearningpedagogy/outcomes.aspx
  19. 19.
    Reich, J.: MOOC Completion and Retention in the Context of Student Intent (2014). https://er.educause.edu/articles/2014/12/mooc-completion-and-retention-in-the-context-of-student-intent/
  20. 20.
    Renz, J., Schwerer, F., Meinel, C.: openSAP: evaluating xMOOC usage and challenges for scalable and open enterprise education. In: Proceedings of the 8th International Conference on E-Learning in the Workplace (2016). ISBN 978-0-9827670-6-1CrossRefGoogle Scholar
  21. 21.
    Rohloff, T., Renz, J., Bothe, M., Meinel, C.: Supporting multi-device e-learning patterns with second screen mobile applications. In: Proceedings of the 16th World Conference on Mobile and Contextual Learning, pp. 25:1–25:8. ACM (2017).  https://doi.org/10.1145/3136907.3136931
  22. 22.
    Rzepka, S.: Lifelong learning in context - from local labor markets to the world wide web. Ph.D. thesis. Ruhr-Universität Bochum, Universitätsbibliothek (2018)Google Scholar
  23. 23.
    Scheffel, M., Drachsler, H., Toisoul, C., Ternier, S., Specht, M.: The proof of the pudding: examining validity and reliability of the evaluation framework for learning analytics. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 194–208. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66610-5_15CrossRefGoogle Scholar
  24. 24.
    Staubitz, T., Meinel, C.: Automated online proctoring of MOOCs - first results. In: Envisioning Report on Quality and Recognition of MOOCs. EADTU (2018, submitted)Google Scholar
  25. 25.
    Wilkowski, J., Deutsch, A., Russell, D.M.: Student skill and goal achievement in the mapping with Google MOOC. In: Proceedings of the First ACM Conference on Learning @ Scale Conference, L@S 2014, pp. 3–10. ACM (2014).  https://doi.org/10.1145/2556325.2566240
  26. 26.
    Yeomans, M., Reich, J.: Planning prompts increase and forecast course completion in massive open online courses. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, LAK 2017, pp. 464–473. ACM (2017).  https://doi.org/10.1145/3027385.3027416
  27. 27.
    Zhang, X., Li, C., Li, S.W., Zue, V.: Automated segmentation of MOOC lectures towards customized learning. In: IEEE 16th International Conference on Advanced Learning Technologies (ICALT 2016), pp. 20–22 (2016).  https://doi.org/10.1109/ICALT.2016.25
  28. 28.
    Zheng, S., Rosson, M.B., Shih, P.C., Carroll, J.M.: Understanding student motivation, behaviors and perceptions in MOOCs. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW 2015, pp. 1882–1895. ACM (2015).  https://doi.org/10.1145/2675133.2675217
  29. 29.
    Zimmerman, B.J.: Models of self-regulated learning and academic achievement. In: Zimmerman, B.J., Schunk, D.H. (eds.) Self-Regulated Learning and Academic Achievement: Theory, Research, and Practice, pp. 1–25. Springer, New York (1989).  https://doi.org/10.1007/978-1-4612-3618-4_1CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Hasso Plattner InstitutePotsdamGermany

Personalised recommendations